Algorithmic activities, like profiling, can lead to challenges for autonomy and informational privacy.
An example is profiling, the extensive collection of data which several companies conduct on their users. This practice is transforming the way we understand privacy, or the lack thereof; as our personal data become the fuel behind a company’s product.
Tools for Privacy
OpenMined is an open source community, with a well-documented repository on GitHub, that contributes to code-based solutions to aspects related to algorithmic safety – in particular privacy protection. The community are creating an accessible ecosystem of tools for private, secure, multi-owner governed AI by extending popular libraries like TensorFlow and PyTorch with advanced techniques in cryptography and private machine learning including: federated learning, differential privacy, multi-party computation, homomorphic encryption, consensus and threshold governance.
TensorFlow Privacy is a GitHub Library that is designed to make it easier for developers to train machine-learning models with privacy, and for researchers to advance the state of the art in MK with strong privacy guarantees. It is based on the principles of differentiated privacy. A technical white-paper is available which describes the privacy mechanisms in more detail.
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